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Record W3049663626 · doi:10.21037/aoe-2020-15

The impact of prehabilitation on surgical outcomes

2021· article· en· W3049663626 on OpenAlexaff
Enrico Maria Minnella, Kenneth Drummond, Francesco Carli

Bibliographic record

VenueAnnals of Esophagus · 2021
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsPrehabilitationMedicineSurgical stressEsophagectomyPerioperativeSarcopeniaEsophageal cancerDiseaseMalnutritionIntensive care medicineWastingPsychological interventionSurgeryCancerPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

Esophageal adenocarcinoma is a particularly challenging and deadly disease. Predominantly affecting older males, the clinical manifestations of the disease include dysphagia, unintentional weight loss, muscle wasting, and persistent functional decline. Due to the aggressive nature of the disease and treatments, patients are at an increased risk for malnutrition, sarcopenia, frailty and postoperative morbidity. Currently, the gold standard for disease management includes perioperative chemo(radio)therapy with esophagectomy, as it confers the greatest curative potential. Though necessary, preoperative anti-cancer therapies impose a significant physiological stress that is poorly tolerated by most patients, resulting in a reduced functional capacity. It can also provoke impairments in physical, nutritional, and psychological status. Despite surgical advancements, esophagectomy remains an invasive procedure associated with a high degree of postoperative morbidity. The disease and treatments adversely impact several determinants of health, and accordingly, an emphasis has been placed on the requisite for multidisciplinary interventions. In an effort to minimize surgical stress, postoperative complications and length of hospital stay, the enhanced recovery after surgery (ERAS) society recently published guidelines for a multidisciplinary approach to perioperative esophagectomy management. Still, postoperative morbidity remains high, therefore highlighting the need for initiatives of broader scope, outside of the surgical setting. The preoperative period represents a window of opportunity for patients to address health deficits and prepare for the stress of esophageal cancer treatments. Prehabilitation describes a multimodal intervention program to optimize a patient’s physical condition preoperatively and minimize the functional declines resultant of cancer treatments. Prehabilitation has been demonstrated to be effective in oncologic surgical candidates to improve functional capacity, postoperative outcomes and rate of recovery. Prehabilitation for esophageal cancer is in its infancy, and to date has not shown any significant impacts on postoperative morbidity. Nevertheless, the few studies published highlight its potential to preserve functional capacity, improve tolerance to the stress of therapies and enhance esophageal cancer care. Still, there is a need for more robust longitudinal studies with larger samples sizes to determine the optimal esophageal prehabilitation prescription and effectively evaluate its impact on both short and long-term outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.460
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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